The present disclosure is directed to methods and systems for calculating insurance premiums based on an analysis of driver behavior, and verification of the actual driver for whom the behavior is being analyzed.
Current programs which determine automobile insurance premiums are based on data collected on external devices on the vehicle. These devices include those with on board diagnostic sensors, and are also black boxes and other sensors, which detect, for example, location, speed, and acceleration, of a vehicle. By analyzing the data from the sensors, a determination of the driving behavior is made, from which automobile insurance premiums are calculated. For example, a “hard” braking event is detected by rapid decreases in speed and acceleration. Such hard breaking events are typically associated with inattentive or distracted drivers, who have to come to a quick stop based on their inattentiveness, which is bad driving behavior. Such bad driving behavior is typically associated with a high risk of accidents, hence, this driver will pay higher insurance premiums, when compared to a driver deemed to be “safer” or a lower risk.
However, this sensor data alone is not enough, and may not provide all of the facts about the particular driving behavior, and the event from which it is based. This is because not all data detected, which initially appears to be bad or unsafe driving behavior, is actually indicative of bad or unsafe driving. For example, when the sensors detect a hard braking event, it may not be the fault of an inattentive or distracted driver having to come to a quick stop, but rather, a driver forced to brake suddenly to avoid colliding with another vehicle entering their lane or driving path. Here, the driver has acted correctly and safely. However, without more than the data from the sensors, there is not any way of knowing that this action was actually a safe and correct driving behavior.